In [ ]:
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import tree

from sklearn.tree import DecisionTreeClassifier
In [ ]:
df = pd.read_csv('vgsales_17.csv')
df.dropna()
df
Out[ ]:
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales
0 8861 298.0 12 1.0 10.0 71.0 0.07 0.06 0.00 0.02 0.15
1 16363 409.0 15 1.0 1.0 93.0 0.00 0.00 0.01 0.00 0.01
2 3476 167.0 11 1.0 4.0 48.0 0.32 0.22 0.00 0.04 0.58
3 7432 104.0 12 1.0 0.0 70.0 0.10 0.08 0.00 0.03 0.21
4 501 65.0 12 1.0 8.0 100.0 1.51 1.12 0.01 0.24 2.89
... ... ... ... ... ... ... ... ... ... ... ...
548 5776 207.0 12 1.0 3.0 10.0 0.15 0.12 0.00 0.04 0.31
549 662 49.0 14 1.0 0.0 10.0 1.00 0.76 0.26 0.36 2.38
550 7811 196.0 12 1.0 6.0 50.0 0.09 0.07 0.00 0.02 0.19
551 2032 411.0 5 1.0 4.0 100.0 0.73 0.26 0.01 0.03 1.02
552 9577 429.0 23 1.0 0.0 8.0 0.10 0.03 0.00 0.00 0.13

553 rows × 11 columns

In [ ]:
from sklearn.model_selection import train_test_split

Y = df['Platform']
X = df.drop(columns='Platform')

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, 
                                                    test_size=0.2, 
                                                    random_state=42)
In [ ]:
#with
dtc = DecisionTreeClassifier(max_depth=5,
                                  min_samples_leaf=4,
                                  max_leaf_nodes=200)
dtc = dtc.fit(X_train.iloc[::1], Y_train.iloc[::1])
plt.figure(figsize=(70,70))
tree.plot_tree(dtc, filled=True, fontsize=10)
Out[ ]:
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In [ ]:
#without
dtc_without = DecisionTreeClassifier()
dtc_without = dtc_without.fit(X_train.iloc[::1], Y_train.iloc[::1])
plt.figure(figsize=(70,70))
tree.plot_tree(dtc_without, filled=True, fontsize=10)
Out[ ]:
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In [ ]:
from sklearn import metrics

Y_predicts = dtc.predict(X_test)
Y_predicts_W = dtc_without.predict(X_test)
In [ ]:
con_mat = metrics.confusion_matrix(Y_test, Y_predicts)
con_mat
Out[ ]:
array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 5, 0, 0, 1, 0, 0, 1, 0, 3, 0, 2, 0, 0, 0, 0, 1, 3],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7],
       [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 2],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 1, 0, 0, 0, 0, 3, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 1, 0, 0, 0, 3, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 1, 3, 0, 0, 0, 0, 0, 1],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 3, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 5]],
      dtype=int64)
In [ ]:
con_mat_w = metrics.confusion_matrix(Y_test, Y_predicts_W)
con_mat_w
Out[ ]:
array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0],
       [0, 0, 5, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 2, 0, 0, 1, 1, 0, 1, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 4, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0],
       [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
       [0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 0, 0, 0, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 3, 0, 1, 0, 1],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0, 0, 0, 0, 0, 3, 1, 1],
       [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
      dtype=int64)
In [ ]:
sns.heatmap(con_mat, annot=True, fmt="d")
plt.xlabel("Предсказания")
plt.ylabel("Реально")
Out[ ]:
Text(50.722222222222214, 0.5, 'Реально')
In [ ]:
sns.heatmap(con_mat_w, annot=True, fmt="d")
plt.xlabel("Предсказания")
plt.ylabel("Реально")
Out[ ]:
Text(50.722222222222214, 0.5, 'Реально')
In [ ]:
print("Точность", metrics.accuracy_score(Y_test, Y_predicts))
print("Чувствительность", metrics.recall_score(Y_test, Y_predicts, average='macro'))
print("Аккуратность", metrics.precision_score(Y_test, Y_predicts, average='macro'))
print("Финальная", metrics.f1_score(Y_test, Y_predicts, average='macro'))

Точность 0.2702702702702703
Чувствительность 0.22900148097516518
Аккуратность 0.247991517909309
Финальная 0.1849233612391507

c:\Users\olp20\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
In [ ]:
print("Точность", metrics.accuracy_score(Y_test, Y_predicts_W))
print("Чувствительность", metrics.recall_score(Y_test, Y_predicts_W, average='macro'))
print("Аккуратность", metrics.precision_score(Y_test, Y_predicts_W, average='macro'))
print("Финальная", metrics.f1_score(Y_test, Y_predicts_W, average='macro'))

Точность 0.3153153153153153
Чувствительность 0.22294126328217237
Аккуратность 0.24767316017316016
Финальная 0.2192611315887178

c:\Users\olp20\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
c:\Users\olp20\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))